PARP Inhibition in Colorectal Cancer—A Comparison of Potential Predictive Biomarkers for Therapy

<b>Background/Objectives</b>: PARP inhibitors (PARPis) currently play frontline roles in the management of prostate, pancreatic, ovarian and breast cancers, but their roles in colorectal cancer (CRC) management have yet to be clarified. Importantly, the specific predictive biomarkers for PARPis in CRC are still matters of investigations. The aim of this study is to identify the potential predictive biomarkers of PARP inhibition in CRC. <b>Methods</b>: Gene set enrichment analyses (GSEAs) and drug ontology enrichment analyses (DOEAs) of PARPi response gene sets were applied as the surrogates of PARPi response to two CRC cohorts in order to compare the predictive capacities of <i>TP53</i> mutation status, MSI status, as well as <i>PARP1</i> and <i>PARP2</i> expression for PARP inhibition to those of a homologous repair deficiency surrogate, and large-scale state transition (LST). Differential enrichment score (ES) and ontology enrichment (OE) analyses were used to interrogate the differential correlation of the predictive biomarkers with PARPi response, relative to LST. <b>Results</b>: The results demonstrated that LST-low, rather than LST-high, CRC subsets exhibited an enrichment of the PARPi response, in contrast to what has been established for other cancers. Furthermore, CRC subsets with wild-type <i>TP53</i>, positive MSI, as well as high <i>PARP1</i> and <i>PARP2</i> expression exhibited an enrichment of the PARPi response gene sets. Moreover, there was no differential enrichment of the PARPi response between LST and each of the MSI statuses, <i>PARP1</i> expression and <i>PARP2</i> expression. Furthermore, the preliminary differential enrichment observed between the LST-based and <i>TP53</i> mutation status-based PARPi responses could not be validated with further testing. <b>Conclusions</b>: MSI status, <i>TP53</i> mutation status as well as <i>PARP1</i> and <i>PARP2</i> expression may be substitutes for low LST as predictive biomarkers of PARPi response in CRC.